Design and Modeling of a Multi-camera-based Disease Detection Model

Abstract A state-of-the-art approach for plant disease detection systems is discussed in this paper. Most proposed disease detection models in literature utilize single infeed cameras to capture the images of sample plant organs for classification. Single-input cameras might compromise the classification accuracy of these models depending on which plant organ is being used. Single input camera classification models have operated with high classification accuracy and efficiency with leaf samples because of their flat surface area nature, however, this is not always the case for fruit samples because of their general spherical or cylindrical nature such as oranges or bananas. The symptoms of a disease on the surface area of a sample fruit might not be distributed evenly, hence a single input camera sensor might miss the vital diseased part if the sample is orientated such that the diseased area is directly opposing to the line of sight of the camera sensor, which can consequently lead to an incorrect classification of that sample under evaluation. Hence, this study has proposed a multi-camera input fruit disease classification model aiming to solve this problem. Citrus orange fruits were used to demonstrate the capability of the proposed model to classify healthy and black rot-affected orange samples. A healthy sample and two black-rot-affected oranges, one with even and the other with uneven distribution of black rot symptoms, were put under evaluation of the proposed multi-camera input model and the classification accuracy was 100% when utilizing a deep learning Convolutional Neural Network classification algorithm.